The Effect of Workload and Task Priority on Multitasking Performance and Reliance on Level 1 Explainable AI (XAI) Use.

IF 2.9 3区 心理学 Q1 BEHAVIORAL SCIENCES
Jawad Alami, Mohamad El Iskandarani, Sara Lu Riggs
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引用次数: 0

Abstract

ObjectiveThis study investigates the effects of workload and task priority on multitasking performance and reliance on Level 1 Explainable Artificial Intelligence (XAI) systems in high-stakes decision environments.BackgroundOperators in critical settings manage multiple tasks under varying levels of workload and priority, potentially leading to performance degradation. XAI offers opportunities to support decision making by providing insights into AI's reasoning, yet its adoption and effectiveness in multitasking scenarios remain underexplored.MethodThirty participants engaged in a simulated multitasking environment, involving UAV command and control tasks, with the assistance of a Level 1 (i.e., basic perceptual information) XAI system on one of the tasks. The study utilized a within-subjects experimental design, manipulating workload (low, medium, and high) and AI-supported-task priority (low and high) across six conditions. Participants' accuracy, use of automatic rerouting, AI miss detection, false alert identification, and use of AI explanations were measured and analyzed across the different experimental conditions.ResultsWorkload significantly hindered performance on the AI-assisted task and increased reliance on the AI system especially when the AI-assisted task was given low priority. The use of AI explanations was significantly affected by task priority only.ConclusionAn increase in workload led to proper offloading by relying on the AI's alerts, but it also led to a lower rate of alert verification despite the alert feature's high false alert rate.ApplicationThe findings from the present work help inform AI system designers on how to design their systems for high-stakes environments such that reliance on AI is properly calibrated.

工作量和任务优先级对多任务性能的影响以及对一级可解释人工智能(XAI)使用的依赖。
目的研究高风险决策环境下,工作负荷和任务优先级对多任务处理绩效和对一级可解释人工智能(XAI)系统依赖的影响。关键环境中的操作员在不同的工作负载和优先级下管理多个任务,这可能导致性能下降。XAI通过提供对人工智能推理的洞察,为支持决策提供了机会,但它在多任务场景中的采用和有效性仍未得到充分探索。方法30名参与者参与了一个模拟多任务环境,涉及无人机指挥和控制任务,在其中一个任务的1级(即基本感知信息)XAI系统的帮助下。该研究采用了受试者内部实验设计,在六种情况下操纵工作量(低、中、高)和人工智能支持的任务优先级(低、高)。在不同的实验条件下,测量和分析了参与者的准确性、自动重新路由的使用、人工智能遗漏检测、假警报识别和人工智能解释的使用。结果工作量显著阻碍了人工智能辅助任务的执行,增加了对人工智能系统的依赖,特别是当人工智能辅助任务的优先级较低时。人工智能解释的使用仅受任务优先级的显著影响。结论工作量的增加导致依靠人工智能的警报进行适当的卸载,但也导致警报功能的高假警报率导致警报验证率较低。应用本工作的发现有助于告知人工智能系统设计师如何为高风险环境设计他们的系统,从而正确校准对人工智能的依赖。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Human Factors
Human Factors 管理科学-行为科学
CiteScore
10.60
自引率
6.10%
发文量
99
审稿时长
6-12 weeks
期刊介绍: Human Factors: The Journal of the Human Factors and Ergonomics Society publishes peer-reviewed scientific studies in human factors/ergonomics that present theoretical and practical advances concerning the relationship between people and technologies, tools, environments, and systems. Papers published in Human Factors leverage fundamental knowledge of human capabilities and limitations – and the basic understanding of cognitive, physical, behavioral, physiological, social, developmental, affective, and motivational aspects of human performance – to yield design principles; enhance training, selection, and communication; and ultimately improve human-system interfaces and sociotechnical systems that lead to safer and more effective outcomes.
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